Purpose-The purpose of this paper is to suggest a way to support work-integrated learning for knowledge work, which poses a great challenge for current research and practice. Design/methodology/approach-The authors first suggest a workplace learning context model, which has been derived by analyzing knowledge work and the knowledge sources used by knowledge workers. The authors then focus on the part of the context that specifies competencies by applying the competence performance approach, a formal framework developed in cognitive psychology. From the formal framework, a methodology is then derived of how to model competence and performance in the workplace. The methodology is tested in a case study for the learning domain of requirements engineering. Findings-The Workplace Learning Context Model specifies an integrative view on knowledge workers' work environment by connecting learning, work and knowledge spaces. The competence performance approach suggests that human competencies be formalized with a strong connection to workplace performance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosis and competency gap analysis can be embedded into the normal working tasks and learning interventions can be offered accordingly. The results of the case study indicate that experts were generally in moderate to high agreement when assigning competencies to tasks. Research limitations/implications-The model needs to be evaluated with regard to the learning outcomes in order to test whether the learning interventions offered benefit the user. Also, the validity and efficiency of competency diagnosis need to be compared to other standard practices in competency management. Practical implications-Use of competence performance structures within organizational settings has the potential to more closely relate the diagnosis of competency needs to actual work tasks, and to embed it into work processes. Originality/value-The paper connects the latest research in cognitive psychology and in the behavioural sciences with a formal approach that makes it appropriate for integration into technology-enhanced learning environments.

Workplace learning happens in the process and context of work, is multi-episodic, often informal, problem based and takes place on a just-in-time basis. While this is a very effective means of delivery, it also does not scale very well beyond the immediate context. We review three types of technologies that have been suggested to scale learning and three connected theoretical discourses around learning and its support. Based on these three strands and an in-depth contextual inquiry into two workplace learning domains, health care and building and construction, four design-based research projects were conducted that have given rise to designs for scaling informal learning with technology. The insights gained from the design and contextual inquiry contributed to a model that provides an integrative view on three informal learning processes at work and how they can be supported with technology: (1) task performance, reflection and sensemaking;(2) help seeking, guidance and support; and (3) emergence and maturing of collective knowledge. The model fosters our understanding of how informal learning can be scaled and how an orchestrated set of technologies can support this process.

<p>Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role of the teacher is being transformed into that of a facilitator. This change inevitably means a reduced level of control on the part of the teacher. This is evidenced, for example, in difficulties experienced in retaining the necessary levels of control when the learning process moves away from institutionally maintained systems to blog-based personal learning environments. In conducting a course in a formal education setting however, it is still essential for the teacher to retain control over certain learning activities, such as course enrolment, assignments, and the assessment process.</p><p>A course management plug-in for the WordPress blog platform called <em>LePress</em> was designed and developed as a possible solution to this problem. By using LePress, teachers are able to more easily manage and coordinate courses in a distributed blog-based environment. Teachers are able to regain control over some important aspects of online course management, while maintaining the learners’ freedom and choice for self-directed learning. This paper documents the results of a survey of a group of 37 teachers who used LePress for at least six months. The study demonstrates that by using LePress, teachers experienced an enhanced level of control over several aspects of the course and this reinforced their perception about the ease of use of the system.</p>

Designing intelligent services for workplace learning presents a special challenge for researchers and developers of learning technology. One of the reasons is that considering learning as a situated and social practice is nowhere so important than in the case where learning is tightly integrated with workplace practices. The current paper analyses the experience of more than 10 years of research intending to offer intelligent services through capturing and leveraging knowledge structures in workplace learning. The reflection looks at results of several European research projects that have promoted this view. From this analysis, I arrive at a dichotomy of guidance versus emergence that describes how the technologies foregrounded one or the other, and what the effects of these design choices have been. I derive conclusions for dealing with this design trade‐off in terms of conceptual, technological and empirical research.

When interacting with social tagging systems, humans exercise complex processes of categorization that have been the topic of much research in cognitive science. In this paper we present a recommender approach for social tags derived from ALCOVE, a model of human category learning. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific resource, such as latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We attribute this to the fact that our approach processes semantic information (either latent topics or external categories) across the three different layers. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.